CHAOS MR \(\rightarrow \)  Multi-Atlas Labeling Beyond the Cranial Vault: the second domain adaptation task consists of data frames from two different dataset. As source domain we, consider the the 2019 CHAOS MR dataset [1]}, previously used in the 2019 CHAOS Grad Challenge. The dataset consists of both MR and CT scans with segmentation maps for the following abdominal organs: liver, right kidney, left kidney and spleen. Similar to [2]} we use the T2-SPIR MR images as the source domain. Each scan is centered to zero mean and unit variance, and values more than three standard deviations away from the mean are clipped. In total, we obtain 20 MR scans, 16 of which we use for training and 4 for validation.
The target domain is represented by the dataset which was presented in the Multi-Atlas Labeling Beyond the Cranial Vault MICCAI 2015 Challenge [3]}. We utilize the 30 CT scans in the training set which are provided segmentation maps, and use 24 for adaptation and 6 for evaluating generalization performance. The value range in the CT scans was first clipped to \([-125,275]\)  HU following literature [4]}. The images were re-sampled to an axial view size of \(256\times 256\) . Background was then cropped such that the distance between any labeled pixel and the image borders is at least 30 pixels, and scans were again resized to \(256\times 256\) . Finally, each 3D scan was normalized independently to zero mean and unit variance, and values more than three standard deviation from the mean were clipped. Data augmentation was performed as follows on both the training MR and training CT instances to improve generalization: (1) random rotations of up to 20 degrees, (2) negating pixel values, (3) adding random Gaussian noise, (4) random cropping.
